B.S. Manjunath

Papers

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About

B.S. Manjunath is a pioneering researcher in computer vision, image analysis, and human-robot interaction, with a career spanning foundational work in multimedia systems to cutting-edge deep learning applications. His major contributions include advancing vision-based gesture recognition for seamless human-robot collaboration, as demonstrated in his 2020 study on using synthetic data to train RGB-based deep learning models for gesture control (e.g., "follow me" commands). This work addresses the critical challenge of data scarcity by generating high-quality annotated training sets, enabling robust robot perception in real-world teaming scenarios. With over 30 citations for this paper alone, Manjunath’s research has significantly impacted the fields of autonomous systems and interactive robotics. He is also widely recognized for his seminal contributions to image segmentation, texture analysis, and content-based image retrieval, including the influential Berkeley Segmentation Dataset and Benchmark. His work has garnered thousands of citations, reflecting its enduring influence on both academic research and practical applications in surveillance, medical imaging, and autonomous navigation. Manjunath’s ability to bridge theoretical foundations with real-world deployment makes him a leading figure in vision-based human-robot interaction.